## AI applied estimation of learning ρ and λ method

2018/10/01 Yasunori Ushiro (Kanagawa University)

2018/12/03 Addition Learning λ Method

### 1. Features of learning ρ and λ method

The λ method is a deciphering method in which the ρ method is used for parallel calculation.

The learning λ method is an improvement of the learning ρ method,

and the efficiency of parallel learning is good.

(1) Discovery of learning ability

Discover fixed method for trajectory group of ρ method

Learning ρ method must be solved once, but learning λ method is unnecessary.

(2) Relationship between learning volume and decoding time

The decoding speed of elliptic curve cryptography improves in proportion to the amount of learning.

Compared to ρ method, the learning ρ method is improved by 1,200 times,

the learning λ method is improved by 10,000 times in performance.

(3) Evolution possibility

Application of AI is expected from the use of arbitrary points and fixation of trajectory group

### 2. Goal of AI application (Change from learning ρ method to learning λ method)

(1) Initial application of AI

1,000,000 times faster than ρ method. Three years later (2021)

It is 34,000 times by the learning ρ method. 30 times faster with AI.

(2) AI intermediate period

100,000,000 times faster than ρ method. 6 years later (2024)

It is 100,000 times by the learning λ method. 1,000 times faster with AI.

Current elliptic curve cryptography is yellow signal.

(3) AI application practical period

1000,000,000,000 times faster than λ method. 10 years later (2028)

It is 1,000,000 times by the learning λ method. 1,000,000 times faster with AI.

Current elliptic curve cryptography is red signal.

### 3. Destructive power estimation of the United States (NSA)

Prediction from the discovery λ method discovery history and NSA cryptographic capability.

(1) Estimated present (2018)

Learnig ρ:80%, case-(1):50%, case-(2)：30%, case-(3):10%

(2) Estimated five years later (2023)

Learnig ρ:100%, case-(1):95%, case-(2):80%, case-(3):50%